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Community Seismic Network

Community Seismic Network. Daniel Obenshain along with K. Mani Chandy, Robert Clayton, Andreas Krause, Michael Olson, Matthew Faulkner, Leif Strand, Rishi Chandy, Daniel Rosenberg, Annie Tang, and others California Institute of Technology. XKCD.

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Community Seismic Network

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  1. Community Seismic Network Daniel Obenshain along with K. Mani Chandy, Robert Clayton, Andreas Krause, Michael Olson, Matthew Faulkner, Leif Strand, Rishi Chandy, Daniel Rosenberg, Annie Tang, and others California Institute of Technology

  2. XKCD Image by Randall Munroe of xkcd.com, Creative Commons Attribution-Noncommercial 2.5 License

  3. Internet and Earthquakes • In the comic: • The tweets travel faster than the earthquake • Other users get quake information before it hits • They are too slow to do anything about it.

  4. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake

  5. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake • Early warning could minimize suffering • Activate safeguards in critical operations

  6. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake • Early warning could minimize suffering • Activate safeguards in critical operations • Providing early warning is an interesting problem • Bayesian decision theory, geology, distributed computing

  7. Background • Earthquakes are dangerous threats • USGS estimates 2000 deaths and $200 billion damages from 7.8 magnitude quake • Early warning could minimize suffering • Activate safeguards in critical operations • Providing early warning is an interesting problem • Bayesian decision theory, geology, distributed computing • Current seismic network is too sparse • Can’t provide enough early warning

  8. Sensor Network is too Sparse A sensor network of one thousand sensors. A sensor network of one hundred sensors. SCSN (Southern California Seismic Network) has ~350 sensors right now.

  9. Pause for Example

  10. Sensor Network is too Sparse Ten thousand sensors! Both a 3 second wave and a 1 second wave.

  11. Early Warning Can Help Slow trains

  12. Early Warning Can Help Slow trains Stop elevators

  13. Early Warning Can Help Slow trains Stop elevators Open fire station doors

  14. Early Warning Can Help • The information can also help the electrical grid. Southern California Edison Territory

  15. Early Warning Can Help • The information can also help the electrical grid. • The grid can be shut down and made safe prior to severe shaking. Southern California Edison Territory

  16. Early Warning Can Help • The information can also help the electrical grid. • The grid can be shut down and made safe prior to severe shaking. • Power back in a day, not weeks after earthquake. Southern California Edison Territory

  17. Benefits • Provide Early Warning • Easy deployment in areas without existing seismic networks • Peru and Indonesia • Cell phones are prevalent • Identify hard-hit areas quickly • Direct first responders

  18. That’s why we’re doing it.What about how we’re doing it?

  19. Expand the Network • We want to add more data.

  20. Expand the Network • We want to add more data. • Why not get data from as many sources as possible?

  21. Expand the Network • We want to add more data. • Why not get data from as many sources as possible? • Add in acceleration devices of different types, cell phones, laptops, etc.

  22. Expand the Network • We want to add more data. • Why not get data from as many sources as possible? • Add in acceleration devices of different types, cell phones, laptops, etc. • The User installs some client software and his or her acceleration data becomes part of the network.

  23. The Client Server Registration Handler Server Alert Listener Error, No Update, or Handlers Registration Handler Sensor Handler Calculation Handler Alert Handler Core processing Handlers and Queues managed Controller Returns Proceed, Error, or New Handlers Registration handler invoked on first run

  24. Example Client – Cell Phone • Measures 3-D acceleration • Program runs in background • Especially good while charging

  25. Example Client – Cell Phone • Martin Lukac (of UCLA) recorded a minor seismic event on a Nokia phone, with different software.

  26. Pause for Example

  27. Picking Algorithm • How often should the client send data to the server?

  28. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring.

  29. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring. • How does the client know?

  30. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring. • How does the client know? • It performs a simple calculation on the incoming data stream.

  31. Picking Algorithm • How often should the client send data to the server? • Only when significant shaking is occurring. • How does the client know? • It performs a simple calculation on the incoming data stream. • We call this the “Picking Algorithm.”

  32. Picking Algorithm STA/LTA > trigger

  33. Picking Algorithm • STA – Short Term Average : the average acceleration over the past several data points STA/LTA > trigger

  34. Picking Algorithm • STA – Short Term Average : the average acceleration over the past several data points • LTA – Long Term Average : the average acceleration over more data points STA/LTA > trigger

  35. Picking Algorithm • STA – Short Term Average : the average acceleration over the past several data points • LTA – Long Term Average : the average acceleration over more data points • trigger – a threshold STA/LTA > trigger

  36. Picking Algorithm Short Term Average Long Term Average Accelerometer

  37. Picking Algorithm Short Term Average Long Term Average New Data Accelerometer

  38. Picking Algorithm Short Term Average Long Term Average Accelerometer

  39. Picking Algorithm • If STA/LTA > trigger is true, then we have “picked.”

  40. Picking Algorithm • If STA/LTA > trigger is true, then we have “picked.” • The algorithm then waits a little bit before sending a message to the server.

  41. Picking Algorithm • If STA/LTA > trigger is true, then we have “picked.” • The algorithm then waits a little bit before sending a message to the server. • This is to make sure it sends data from the peak of the wave.

  42. Picking Algorithm 1 2 3 Pause for this length of time before sending a message to the server. • Detected significant shaking • Maximum shaking • Sent message to server

  43. Picking Algorithm • After sending a message to the server, the client will wait a while before picking again.

  44. Picking Algorithm • After sending a message to the server, the client will wait a while before picking again. • This is to stop the client from picking multiple times for the same shaking.

  45. Picking Algorithm 1 2 Delay for this length of time before picking again. • Last message sent to server • The coda of the earthquake, where we don’t want to pick

  46. Picking Algorithm • Five tunable parameters.

  47. Picking Algorithm • Five tunable parameters. • Length of STA

  48. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA

  49. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA • Value of trigger

  50. Picking Algorithm • Five tunable parameters. • Length of STA • Length of LTA • Value of trigger • How long to wait after picking before sending a message to the server

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